Vision-Based Obstacle Detection and Collision Prevention in Self-Driving Cars

Devi, Y Sarada; Sathvik, S; Ananya, P; Tharuni, P; Vamsi, N Naga Krishna

doi:10.1088/1742-6596/2335/1/012019

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…In [251], authors presented an on-board and UAV-based vision systems for long-range obstacle detection in railways. A CNN-based Obstacle Detection and Collision Prevention developed in [252] which has a Raspberry Pi 4 Model B as its control and processing unit. A support system for visually impaired person is developed in [253] to detect obstacles and notify the person.…”

Section: Cameramentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Golroudbari¹,

Sabour²

2023

Preprint

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This review paper presents a comprehensive overview of end-to-end deep learning frameworks used in the context of autonomous navigation, including obstacle detection, scene perception, path planning, and control. The paper aims to bridge the gap between autonomous navigation and deep learning by analyzing recent research studies and evaluating the implementation and testing of deep learning methods. It emphasizes the importance of navigation for mobile robots, autonomous vehicles, and unmanned aerial vehicles, while also acknowledging the challenges due to environmental complexity, uncertainty, obstacles, dynamic environments, and the need to plan paths for multiple agents. The review highlights the rapid growth of deep learning in engineering data science and its development of innovative navigation methods. It discusses recent interdisciplinary work related to this field and provides a brief perspective on the limitations, challenges, and potential areas of growth for deep learning methods in autonomous navigation. Finally, the paper summarizes the findings and practices at different stages, correlating existing and future methods, their applicability, scalability, and limitations. The review provides a valuable resource for researchers and practitioners working in the field of autonomous navigation and deep learning.

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Section: Cameramentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Golroudbari¹,

Sabour²

2023

Preprint

View full text Add to dashboard Cite

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“…In Franke et al (2022), authors presented on-board and UAV-based vision systems for longrange obstacle detection in railways. A CNN-based Obstacle Detection and Collision Prevention developed in Devi et al (2022) which has a Raspberry Pi 4 Model B as its control and processing unit.…”

Section: Ta B L E 9 Image Segmentation Algorithmsmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

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This review article presents recent advancements in deep learning method-ologies and applications for autonomous navigation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, obstacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limitations, and areas for further development. The review emphasizes the interdisciplinary nature of autonomous navigation and addresses challenges posed by dynamic and complex environments, uncertainty, and obstacles. With a particular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the importance of navigation in these domains. By synthesizing findings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, recent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable resource for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying , evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.

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Section: Ta B L E 9 Image Segmentation Algorithmsmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

View full text Add to dashboard Cite

This review article presents recent advancements in deep learning methodologies and applications for autonomous nav- igation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, ob- stacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limita- tions, and areas for further development. The review em- phasizes the interdisciplinary nature of autonomous naviga- tion and addresses challenges posed by dynamic and com- plex environments, uncertainty, and obstacles. With a par- ticular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the impor- tance of navigation in these domains. By synthesizing find- ings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, re- cent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable re- source for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying, evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.

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Vision-Based Obstacle Detection and Collision Prevention in Self-Driving Cars

Cited by 7 publications

References 10 publications

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

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